Synthetic data for training, used with care
Generate training data with models where it helps, and filter, verify and document it properly
A taste of a lesson
I generated 20,000 support conversations with a large model. Training on them helped on my test set. Is that good news?
Maybe, but first check what the test set is made of. If it was generated the same way, the model may simply have learned the generator's style, which flatters the score. The real question is performance on actual customer conversations. Also look at the data itself: sample 50 generated conversations and note how many repeat the same structure or contain wrong policy details. Then check the generating model's terms allow training on its outputs. Quick exercise: describe one way to build a small real test set from your existing support logs while keeping customer details out.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Identify tasks where synthetic data helps and where it is likely to hurt
- Design generation with real seeds and a taxonomy for diversity
- Filter synthetic examples with automatic checks and human sampling
- Explain model collapse, bias amplification and terms of use risks
- Evaluate on real data and document the provenance of synthetic sets
Lesson plan
- 1 Where synthetic data helps Match synthetic data to real gaps such as rare cases, new formats or scarce languages. Start
- 2 Generating with diversity Seed generation with real examples and a taxonomy so outputs cover the space. Start
- 3 Filtering and verification Remove wrong, unsafe or low quality examples before training. Start
- 4 Collapse, bias and drift Understand the risks of training on model outputs, especially repeatedly. Start
- 5 Terms, privacy and documentation Check permissions and record exactly how synthetic data was made. Start
- 6 Mixing and evaluating on real data Combine synthetic and real data and judge the result only on real held out examples. Start
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About this tutor
For practitioners and curious learners who want to use model generated data to train or fine tune models without fooling themselves. You will learn where synthetic data genuinely helps, such as covering rare cases or bootstrapping instruction data, and where it quietly hurts. The lessons cover seeding generation with real examples, controlling diversity with a task taxonomy, automatic and human filtering, the risk of models degrading when trained repeatedly on their own outputs, bias amplification, and the licence and terms questions around using one model's outputs to train another. Throughout, the rule is simple: evaluate on real data, and keep a record of what was generated and how.
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About the teacher
Fine tuning with judgment: when to do it, how to do it well, and how to know it worked
9 tutors 428 lessons taught Sample
I teach fine tuning and post training: choosing between prompting, retrieval and tuning, building datasets, parameter efficient methods, instruction and preference tuning, and evaluating the result. My background is in applied machine learning projects where the expensive mistake was usually tuning a model before anyone had defined what better meant. That is why I start every topic with the evaluation...
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